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TensorRT RTX samples pair ONNX Runtime with native C++ inference

developer.nvidia.com by Luca Spindler 4 min read Editor score 8/10

The samples separate Python-based model export to ONNX from C++ deployment, with reported GPU results including 39× real-time Nemotron ASR and 38.3 FPS for SAM 2.1. Engineers building local inference apps can use the examples to evaluate deployment patterns and acceleration.

What: DIN Deploy is an open-source collection of C++ samples combining ONNX Runtime with the TensorRT RTX execution provider for local inference.
How: Python exporters convert Hugging Face checkpoints to ONNX artifacts; native C++ CLIs run inference through ONNX Runtime APIs.
Why it matters: Separating export from deployment gives engineers shared C++ inference code with optional hardware-specific acceleration.

The samples export checkpoints to ONNX, then run inference through C++ and ONNX Runtime. Sketched from the article's own text.

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